US-1: Full AI Nationalization can cause Misaligned Economic Incentives

The paper offers a timely policy analysis of a full United States nationalization of frontier AI labs and argues that such a move could create misaligned economic incentives that slow diffusion and reduce overall welfare. It surveys historical precedents like the USRA, Manhattan Project, Apollo Program, MITI, and Korea’s heavy-industry drive, then applies public-choice theories such as Niskanen’s budget maximizing bureaucracy and Kornai’s soft budget constraint to foresee cost overruns and efficiency losses. The narrative is well structured and the prose is clear. The inclusion of concrete channels like talent retention, compute commandeering under the Defense Production Act, and security driven restrictions on collaboration grounds the discussion in plausible mechanisms. The historical vignettes and theories are drawn together coherently and the paper ends with pragmatic recommendations that nationalization should be a last resort in favor of “soft” public-private control. ​

The contribution is mainly descriptive and lacks formal modeling or new empirical evidence. No quantitative framework is provided to compare nationalized and private incentive structures, nor are there back-of-the-envelope fiscal estimates beyond citing past GDP percentages for the Manhattan and Apollo projects. The historical cases are summarized but not tested for external validity in the AI context. Recent literature on compute governance, state capacity in technology races, and AI alignment economics is largely missing, so the intellectual foundation rests on a limited set of classic public-choice sources.

AI safety relevance is present but indirect. The paper stresses that misaligned incentives under nationalization could hinder diffusion and perhaps heighten safety risks, yet it does not trace how a public monopoly would affect catastrophic misuse probabilities, alignment R&D funding, or global compute races. A more explicit mapping from ownership structure to safety outcomes would strengthen the impact.

Technical quality and documentation are modest. The essay is properly referenced and the parsed PDF contains tables and a Bloomberg chart, but no data, code, or appendices accompany the narrative, making replication or further analysis impossible. The policy recommendations are sensible yet untested and rely on qualitative reasoning alone.

I'm grading this more as a policy paper than a quantitative economics paper.

I enjoyed reading this! This was well-situated in the nationalization debates. You've identified the core existing literature (mostly micro-sites, but that's the AI field for you). Good historical overview of nationalization case studies. I hadn't considered the USG recruiting tools at all. Your points on nationalization causing underperforming firms were also fascinating. These feel like a novel arguments to me in the AI nationalization context. I thought your recommendations section was reasonably thorough. One place to expand could be why you prioritize catastrophic risk mitigation. I expect this is because you expect those risks to justify nationalization, but spelling that out would have been helpful.

Overall, well done!

The paper engages well with dynamics established during previous nationalisation attempts. However, it would benefit greatly from more cleanly specifying the gradient of possible nationalisation options and how the trade-offs between them differ (which given some greater degree of goverment involvement on the security side would be especially helpful for informing policymakers about how far to go). Additionally, while the paper highlights some trade-offs (loss of diffusion, talent flight, bureaucratic drag), it doesn't specifcally quantify most of the benefits and costs, which would be vital for informing policymakers about the trade-off of possible approaches, and for mitigations to reduce their impact. Many industries have previously been nationalised (especially post-WW2 in Western Europe), so there is plenty of scope for work - and much pre-existing work - on the effect of this on innovation.